This is something I’ve been thinking about as well, and I think you do a good job explaining it. There’s definitely more to breakdown and analyze within competence and intelligence. Such as simulation being a distinct sort of part of intelligence. A measure of how many moves a player can think ahead in a strategy game like chess or Go. How large of a possibility-tree can they build in the available time? With what rate of errors? How quickly does the probability of error increase as the tree increases in size? How does their performance decrease as the complexity of the variables needed to be tracked for an accurate simulation increase?
Yeah, I wish we had some cleaner terminology for that. Finetuning the “simulation engine” towards a particular task at hand (i.e. to find the best trade-off between breadth and depth search in strategy games, or even know how much “thinking time” or “error allowance” to allocate to a move), given limited cognitive resources, is something that I would associate with level 3 capability. It certainly seems like learning could go into the direction of making the model of the game more useful by either improving the extent to which this model predicts/ouputs good moves or by improving the allocation of cognitive resources to the sub-tasks involved. Presumably, an intelligent system should be capable of testing which improvement vectors seem most fruitful (and the frequency with which to update this analysis), but I find myself a bit confused about whether that should count as level 3 or as level 4, since the system is reasoning about allocating resources across relevant learning processes.
This is something I’ve been thinking about as well, and I think you do a good job explaining it. There’s definitely more to breakdown and analyze within competence and intelligence. Such as simulation being a distinct sort of part of intelligence. A measure of how many moves a player can think ahead in a strategy game like chess or Go. How large of a possibility-tree can they build in the available time? With what rate of errors? How quickly does the probability of error increase as the tree increases in size? How does their performance decrease as the complexity of the variables needed to be tracked for an accurate simulation increase?
Yeah, I wish we had some cleaner terminology for that.
Finetuning the “simulation engine” towards a particular task at hand (i.e. to find the best trade-off between breadth and depth search in strategy games, or even know how much “thinking time” or “error allowance” to allocate to a move), given limited cognitive resources, is something that I would associate with level 3 capability.
It certainly seems like learning could go into the direction of making the model of the game more useful by either improving the extent to which this model predicts/ouputs good moves or by improving the allocation of cognitive resources to the sub-tasks involved. Presumably, an intelligent system should be capable of testing which improvement vectors seem most fruitful (and the frequency with which to update this analysis), but I find myself a bit confused about whether that should count as level 3 or as level 4, since the system is reasoning about allocating resources across relevant learning processes.